Apparatuses and methods for classifying temporal sections

ABSTRACT

An apparatus is configured to identify a plurality of temporal ranges, associated with a plurality of first identifiers and a plurality of sets of descriptive data, generate a plurality of temporal sections, wherein generating further includes dividing each temporal range into at least a temporal section of the plurality of temporal sections, receive at least a second identifier, wherein the at least a second identifier is associated with at least a temporal constraint and a set of second identifier data, classify, as a function of the inputs, the at least a first identifier to a particular temporal section of the plurality of temporal sections as a function of the plurality of sets of descriptive data and the at least a set of second identifier data and output the particular temporal section.

FIELD OF THE INVENTION

The present invention generally relates to the field of human resourcestechnology. In particular, the present invention is directed toapparatuses and methods for classifying temporal sections.

BACKGROUND

Current ways of creating virtual appointments do not optimizeclassification algorithms to create appropriate matches.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for using machine learning to classifytemporal sections is illustrated. The apparatus includes at least aprocessor and a memory communicatively connected to the processor, thememory containing instructions configuring the processor to identify aplurality of temporal ranges, associated with a plurality of firstidentifiers and a plurality of sets of descriptive data, wherein eachtemporal range of the plurality of temporal ranges is associated a firstidentifier of the plurality of first identifiers and each firstidentifier of the plurality of first identifiers is associated with aset of descriptive data of the plurality of sets of descriptive data,generate a plurality of temporal sections, wherein generating furtherinclude dividing each temporal range into at least a temporal section ofthe plurality of temporal sections, receive at least a secondidentifier, wherein the at least a second identifier is associated withat least a temporal constraint and a set of second identifier data,classify, as a function of the inputs, the at least a first identifierto a particular temporal section of the plurality of temporal sectionsas a function of the plurality of sets of descriptive data and the atleast a set of second identifier data and output the particular temporalsection.

In an aspect, a method for using machine learning to classify temporalsections is illustrated. The method includes using a computing device toidentify a plurality of temporal ranges, associated with a plurality offirst identifiers and a plurality of sets of descriptive data, whereineach temporal range of the plurality of temporal ranges is associated afirst identifier of the plurality of first identifiers and each firstidentifier of the plurality of first identifiers is associated with aset of descriptive data of the plurality of sets of descriptive data,generate a plurality of temporal sections, wherein generating furtherinclude dividing each temporal range into at least a temporal section ofthe plurality of temporal sections, receive at least a secondidentifier, wherein the at least a second identifier is associated withat least a temporal constraint and a set of second identifier data,classify, as a function of the inputs, the at least a first identifierto a particular temporal section of the plurality of temporal sectionsas a function of the plurality of sets of descriptive data and the atleast a set of second identifier data and output the particular temporalsection.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of an apparatus forclassifying virtual temporal sections;

FIG. 2 is a block diagram of exemplary embodiment of a machine learningmodule;

FIG. 3 illustrates an exemplary nodal network;

FIG. 4 is a block diagram of an exemplary node;

FIG. 5 is a graph illustrating an exemplary relationship between fuzzysets;

FIG. 6 is a flow diagram of an exemplary method for classifying virtualtemporal sections;

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatuses and methods for creating virtual appointments between arecruiter/hiring entity and a job seeker. In an embodiment, temporalsections may include job interviews conducted during an event.

Aspects of the present disclosure can be used by an entity whichoperates an employment or job matching service which may be utilized byjob seekers, employers and/or staffing agencies to create optimalmatches for job interviews based on attendees to an event such as acareer fair.

Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of apparatus 100 forusing machine learning to classify temporal sections is illustrated.Apparatus 100 includes processor 104 and memory 108 communicativelyconnected to processor 104, wherein memory 108 contains instructionsconfiguring processor 104 to carry out the classifying process. In someembodiments, apparatus 100 may include computing device 112. Apparatus100 may be communicatively connected to computing device 112. As used inthis disclosure, “communicatively connected” means connected by way of aconnection, attachment, or linkage between two or more relata whichallows for reception and/or transmittance of information therebetween.For example, and without limitation, this connection may be wired orwireless, direct, or indirect, and between two or more components,circuits, devices, systems, and the like, which allows for receptionand/or transmittance of data and/or signal(s) therebetween. Data and/orsignals therebetween may include, without limitation, electrical,electromagnetic, magnetic, video, audio, radio, and microwave dataand/or signals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital, or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure. Computing device112 may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device 112 may include, be included in,and/or communicate with a mobile device such as a mobile telephone orsmartphone. Computing device 112 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Computing device 112 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 112 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 112 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 112 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 112 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 112 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofapparatus 100 and/or computing device 112.

With continued reference to FIG. 1 , processor 104 and/or computingdevice 112 may be designed and/or configured by memory 108 to performany method, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, processor 104 and/or computing device 112 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 and/orcomputing device 112 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Processor 104 and/or computing device 112 may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine learning processes 116. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses a body of data known as “training data” and/or a“training set” (described further below) to generate an algorithm thatwill be performed by a computing device/module to produce outputs givendata provided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured identify a plurality of temporal ranges, associated with aplurality of first identifiers and a plurality of sets of descriptivedata. Each temporal range of the plurality of temporal ranges isassociated a first identifier of the plurality of first identifiers andeach first identifier of the plurality of first identifiers isassociated with a set of descriptive data of the plurality of sets ofdescriptive data. As used in this disclosure, a “first identifier” is aninterview host participant at an event, such as a job recruiter orhiring entity. A first identifier may participant in a job fair as partof a panel of job recruiters and the like, seeking to interviewpotential job candidates. As used in this disclosure, a “temporal range”is the time availability of a first identifier for appointment purposes.In some embodiments, operators of an event may set a predetermined timeto identify temporal ranges. For example, first identifiers may berequired to submit their availability for interview appointments a weekbefore the career fair or during a specific time while attending theevent. First identifiers may submit their availability using a networkthat processor 104 and/or computing device 112 may be connected to inorder to create temporal sections based on temporal ranges 120. In someembodiments, temporal range 120 may include descriptive data associatedwith a first identifier. Descriptive data, as used in this disclosure,is any first identifier information pertaining to professional areassuch as hiring requirements, work description, companyhistory/description, and the like.

Still referring to FIG. 1 , Processor 104 and/or computing device 112 isconfigured to generate a plurality of temporal sections, whereingenerating includes dividing each temporal range into at least atemporal section of the plurality of temporal sections. As used in thisdisclosure, a “temporal section” is indicator on a schedule thatpertains to the availability and/or unavailability of event attendeesduring certain times. In some embodiments, generating the plurality oftemporal sections is numerical limited. For example, the number oftemporal sections per hour may be limited to a predetermined number tospace out the availability of recruiters, allowing more secondidentifiers the chance to be interviewed. As used in the disclosure, a“second identifier” is an event attendee seeking an interview, such as ajob applicant. For example, the system may have a temporal section maxof 10 recruiters per hour rather than allowing all recruiters to bookfor the same hour during the career fair. Processor 104 and/or computingdevice 112 may create temporal sections using a machine learningalgorithm. For example, time temporal ranges 120 of the firstidentifiers may be an algorithm input and a plurality of temporalsections associated the first identifiers may be the output. Algorithmmay use a set of time limitation rules as part of the training data.Training data, as used in this disclosure, is data containingcorrelations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Still referring to FIG. 1 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 1 , models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training dataset are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data.

Still referring to FIG. 1 , machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude inputs, outputs, and a scoring function representing a desiredform of relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various possible variations ofsupervised machine learning algorithms that may be used to determinerelation between inputs and outputs.

Supervised machine-learning processes may include classificationalgorithms, defined as processes whereby a computing device derives,from training data, a model for sorting inputs into categories or binsof data. Classification may be performed using, without limitation,linear classifiers such as without limitation logistic regression and/ornaive Bayes classifiers, nearest neighbor classifiers, support vectormachines, decision trees, boosted trees, random forest classifiers,and/or neural network-based classifiers.

Still referring to FIG. 1 , machine learning processes may includeunsupervised processes. An unsupervised machine-learning process, asused herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning processmay be free to discover any structure, relationship, and/or correlationprovided in the data. Unsupervised processes may not require a responsevariable; unsupervised processes may be used to find interestingpatterns and/or inferences between variables, to determine a degree ofcorrelation between two or more variables, or the like.

Still referring to FIG. 1 , machine-learning processes as described inthis disclosure may be used to generate machine-learning models. Amachine-learning model, as used herein, is a mathematical representationof a relationship between inputs and outputs, as generated using anymachine-learning process including without limitation any process asdescribed above, and stored in memory; an input is submitted to amachine-learning model once created, which generates an output based onthe relationship that was derived. For instance, and without limitation,a linear regression model, generated using a linear regressionalgorithm, may compute a linear combination of input data usingcoefficients derived during machine-learning processes to calculate anoutput datum. As a further non-limiting example, a machine-learningmodel may be generated by creating an artificial neural network, such asa convolutional neural network comprising an input layer of nodes, oneor more intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

A lazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship. As a non-limiting example, an initialheuristic may include a ranking of associations between inputs andelements of training data. Heuristic may include selecting some numberof highest-ranking associations and/or training data elements. Lazylearning may implement any suitable lazy learning algorithm, includingwithout limitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Still referring to FIG. 1 , Processor 104 and/or computing device 112may be configured to output, as a function of the created temporalsections, first identifier datum 124. As used us in this disclosure,“identify datum” is information pertaining to the set availability of afirst identifier based on temporal sections. Processor 104 and/orcomputing device 112 may be configured to send first identifier datum124 to second identifiers through remote device 128. As used in thisdisclosure, a “remote device” is any electronic and/or computing devicethat the plurality of second identifiers and first identifiers mayreceive information on. For example, remote device 128 may be a laptop,cellphone, pager, smart watch, and the like. In some embodiments, secondidentifier may select the output of first identifier datum 124. Secondidentifier selection may include a first identifier classifier based ondescriptive data. A “classifier” is a machine-learning model, such as amathematical model, neural net, or program generated by a machinelearning algorithm known as a “classification algorithm,” as describedin further detail below, that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. In some embodiments, a plurality of first identifiertemporal sections may be a classifier input, a plurality of firstidentifier descriptive data as a part of the training data, and aplurality of categorized first identifiers as the output. For example, asecond identifier may select to only see the temporal sections of theplurality of first identifiers based on first identifier background,such as electrical engineering firms. The classification algorithm mayoutput the classified first identifiers matching that backgroundcategory for processor 104 and/or computing device 112 to display to thesecond identifier to see the applicable temporal sections.

Still referring to FIG. 1 , classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. Computing device 112 may be configured togenerate a classifier using a NaïveBayes classification algorithm. NaïveBayes classification algorithm generates classifiers by assigning classlabels to problem instances, represented as vectors of element values.Class labels are drawn from a finite set. Naïve Bayes classificationalgorithm may include generating a family of algorithms that assume thatthe value of a particular element is independent of the value of anyother element, given a class variable. Naïve Bayes classificationalgorithm may be based on Bayes Theorem expressed as P(A/B)=P(B/A)P(A)±P(B), where P(AB) is the probability of hypothesis A given data Balso known as posterior probability; P(B/A) is the probability of data Bgiven that the hypothesis A was true; P(A) is the probability ofhypothesis A being true regardless of data also known as priorprobability of A; and P(B) is the probability of the data regardless ofthe hypothesis. A naïve Bayes algorithm may be generated by firsttransforming training data into a frequency table. Computing device 112may then calculate a likelihood table by calculating probabilities ofdifferent data entries and classification labels. Computing device 112may utilize a naïve Bayes equation to calculate a posterior probabilityfor each class. A class containing the highest posterior probability isthe outcome of prediction. Naïve Bayes classification algorithm mayinclude a gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 1 , computing device 112 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute 1 as derived using aPythagorean norm: 1=√(Σ_(i=0){circumflex over ( )}

a_i

{circumflex over ( )}2), where ai is attribute number i of the vector.Scaling and/or normalization may function to make vector comparisonindependent of absolute quantities of attributes, while preserving anydependency on similarity of attributes; this may, for instance, beadvantageous where cases represented in training data are represented bydifferent quantities of samples, which may result in proportionallyequivalent vectors with divergent values.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured receive at least a second identifier, wherein the secondidentifier is associated with at least a temporal constraint 132 and aset of user data. A temporal constraint, as used in this disclosure, isa selection of a temporal section of a first user identifier forappointment making purposes. As used in this disclosure, “user data” isany information on a second identifier pertaining to professional areassuch as, work, educational and/or personal accomplishments,qualifications, interests, and the like. In some embodiments, user datamay be a job resume, cover letter, writing sample, among other things ofa second identifier. In some embodiments, processor 104 and/or computingdevice 112 may acquire a temporal constraint in the form of a bid by asecond identifier. As used in this disclosure, a “bid” is a temporalsection request procedure followed by a second identifier forappointment matching purposes. Bids may include a second identifieruploading the user data to a network which processor 104 and/orcomputing device 112 may access to process a second identifier temporalconstraint. In some embodiments, bids may include a second identifierrequesting to reserve a specific temporal section associated with afirst identifier of the plurality of first identifiers. In someembodiments, bids may require different elements of user data from asecond identifier for a temporal section associated with a particularfirst identifier. For example, for a second identifier create a temporalconstraint 132 for a specific first identifier out of the plurality offirst identifiers, the second identifier may be required to submit intheir bid specific documents, such as a cover letter and writing sample.Bidding may occur during a set time at the virtual event based on thenumber of second identifiers or first identifiers that actually attendthe event. In some embodiments, processor 104 and/or computing device112 may limit the number of temporal constraints 132 received from aparticular second identifier. For example, a second identifier may belimited to a set number of bids that may be submitted throughout theevent. A second identifier may also be limited to a period in which bidsmay be placed. For example, bids may only be accepted before a deadlineset by an event operator. In some embodiments, the user data from theplurality of second identifiers may be classified against thedescriptive data of the plurality of first identifiers, using anyclassification algorithm as described above. For example, a bid withuser data containing nursing experience may be categorized with firstidentifiers dealing in medical and hospitality work. A user dataclassifier may take a plurality of second identifier bids as an input,descriptive data of the plurality of first identifiers as training dataand output a plurality of matches between second identifiers to firstidentifiers based on the user data. In some embodiments, user dataclassifier may use a language processing module to parse elements ofuser data into a readable machine learning format.

Still referring to FIG. 1 , a language processing module may include anyhardware and/or software module. The language processing module may beconfigured to extract, from the one or more documents, one or morewords. One or more words may include, without limitation, strings of oneor more characters, including without limitation any sequence orsequences of letters, numbers, punctuation, diacritic marks, engineeringsymbols, geometric dimensioning and tolerancing (GD&T) symbols, chemicalsymbols and formulas, spaces, whitespace, and other symbols, includingany symbols usable as textual data as described above. Textual data maybe parsed into tokens, which may include a simple word (sequence ofletters separated by whitespace) or more generally a sequence ofcharacters as described previously. The term “token,” as used herein,refers to any smaller, individual groupings of text from a larger sourceof text; tokens may be broken up by word, pair of words, sentence, orother delimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model, describedfurther below.

Still referring to FIG. 1 , the language processing module may operateto produce a language processing model. Language processing model mayinclude a program automatically generated by computing device and/orlanguage processing module to produce associations between one or morewords extracted from at least a document and detect associations,including without limitation mathematical associations, between suchwords. For example, processor 104 and/or computing device 112 may take aterm form an element of user data such as “Patent Attorney” and match toa plurality of first identifiers that contains synonyms such as “USPTOPatent Practitioner Recruiter”. Associations between language elements,where language elements include for purposes herein extracted words,relationships of such categories to other such term may include, withoutlimitation, mathematical associations, including without limitationstatistical correlations between any language element and any otherlanguage element and/or language elements. Statistical correlationsand/or mathematical associations may include probabilistic formulas orrelationships indicating, for instance, a likelihood that a givenextracted word indicates a given category of semantic meaning. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given semantic meaning; positive or negativeindication may include an indication that a given document is or is notindicating a category semantic meaning. Whether a phrase, sentence,word, or other textual element in a document or corpus of documentsconstitutes a positive or negative indicator may be determined, in anembodiment, by mathematical associations between detected words,comparisons to phrases and/or words indicating positive and/or negativeindicators that are stored in memory at computing device, or the like.

Still referring to FIG. 1 , language processing module may generate thelanguage processing model by any suitable method, including withoutlimitation a natural language processing classification algorithm;language processing model may include a natural language processclassification model that enumerates and/or derives statisticalrelationships between input terms and output terms. Algorithm togenerate language processing model may include a stochastic gradientdescent algorithm, which may include a method that iteratively optimizesan objective function, such as an objective function representing astatistical estimation of relationships between terms, includingrelationships between input terms and output terms, in the form of a sumof relationships to be estimated. In an alternative or additionalapproach, sequential tokens may be modeled as chains, serving as theobservations in a Hidden Markov Model (HMM). HMMs as used herein arestatistical models with inference algorithms that that may be applied tothe models. In such models, a hidden state to be estimated may includean association between an extracted words, phrases, and/or othersemantic units. There may be a finite number of categories to which anextracted word may pertain; an HMM inference algorithm, such as theforward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of a first identifier element, and existence ofan inverse element for each vector, and can be multiplied by scalarvalues under an operation of scalar multiplication compatible with fieldmultiplication, and that has a first identifier element is distributivewith respect to vector addition, and is distributive with respect tofield addition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1 , language processing module may use a corpusof documents to generate associations between language elements in alanguage processing module, and diagnostic engine may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory. In an embodiment, language module and/or computing device mayperform this analysis using a selected set of significant documents,such as documents identified by one or more experts as representing goodinformation; experts may identify or enter such documents via graphicalsecond identifier interface, or may communicate first identifiers ofsignificant documents according to any other suitable method ofelectronic communication, or by providing such first identifier to otherpersons who may enter such identifications into computing device.Documents may be entered into a computing device by being uploaded by anexpert or other persons using, without limitation, file transferprotocol (FTP) or other suitable methods for transmission and/or uploadof documents; alternatively or additionally, where a document isidentified by a citation, a uniform resource identifier (URI), uniformresource locator (URL) or other datum permitting unambiguousidentification of the document, diagnostic engine may automaticallyobtain the document using such an identifier, for instance by submittinga request to a database or compendium of documents such as JSTOR asprovided by Ithaka Harbors, Inc. of New York.

Still referring to FIG. 1 , classification of user data may includeranking the user data classifier output datum in order of compatibilityusing a fuzzy inference system described further below. Processor 104and/or computing device 112 is configured to classify a particularsecond identifier of the plurality of second identifiers to a particulartemporal section of the plurality of temporal sections, whereinclassifying may include a plurality of event factors. In someembodiments, event factors may include bidding priority (e.g., first tobid first to reserve), bidding temporal constraint quality (e.g., if asecond identifier submitted the proper user data), bidding limitations(number of bids a second identifier can place may correlate to thenumber of a appointments that may be generated for the secondidentifier), temporal section amendments or abandonment by firstidentifiers, and the like. Processor 104 and/or computing device 112 mayuse any machine learning process described throughout this disclosure,such as a classifier algorithm, to generate appointments between asecond identifier and a first identifier. Appointment classifier 136 maytake bids of the plurality of second identifiers as inputs and output aplurality of appointments between a second identifier and a firstidentifier. The training data may include the plurality of eventfactors, output datum of user data classifier, and first identifierdatum 124 to match each second identifier of the plurality of secondidentifiers to at least one first identifier for appointment purposes.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured to output particular temporal sections 140 between aplurality of first identifiers and a plurality of second identifiers. Asused in this disclosure, a “particular temporal section” is a scheduleshowing the generated appointments between second identifiers and firstidentifiers at an event. In some embodiments, particular temporalsections 140 may be outputted via electronic notification to secondidentifiers and first identifiers. The notification (or alert) mayinvolve an email, text message, automated voice call, and the like,among others sent to remote device 128. Particular temporal sections 140may be outputted freely during the event or systematically. For example,if there is a set time for all second identifiers at the event to submittheir Bids then the appointments may be generated after that set timeperiod has closed for all second identifiers at once. Alternatively, ifthere is no set time period for second identifiers to bid thenappointments may be created between the second identifier and recruiteras they are selected throughout the event.

Referring now to FIG. 2 , an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212.“Training data,” as used herein, is data containing correlations that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data 204 may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data 204 may evince one or more trendsin correlations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data 204 according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data 204 may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training data204 may include data entered in standardized forms by persons orprocesses, such that entry of a given data element in a given field in aform may be mapped to one or more descriptors of categories. Elements intraining data 204 may be linked to descriptors of categories by tags,tokens, or other data elements; for instance, and without limitation,training data 204 may be provided in fixed-length formats, formatslinking positions of data to categories such as comma-separated value(CSV) formats and/or self-describing formats such as extensible markuplanguage (XML), JavaScript Object Notation (JSON), or the like, enablingprocesses or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2 ,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 2 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 2 , machine-learning module 200 may beconfigured to perform a lazy-learning process 220 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 204. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 204 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 2 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2 , machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinputs and outputs, as described above, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 204. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process228 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 2 , machine learning processes may include atleast an unsupervised machine-learning processes 232. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2 , machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 3 , an exemplary embodiment of neural network 300is illustrated. A neural network 300 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 304, one or more intermediate layers 308, and an output layer ofnodes 312. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network, or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 4 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring now to FIG. 5 , an exemplary embodiment of fuzzy setcomparison 500 is illustrated. A first fuzzy set 504 may be represented,without limitation, according to a first membership function 508representing a probability that an input falling on a first range ofvalues 512 is a member of the first fuzzy set 504, where the firstmembership function 508 has values on a range of probabilities such aswithout limitation the interval [0,1], and an area beneath the firstmembership function 508 may represent a set of values within first fuzzyset 504. Although first range of values 512 is illustrated for clarityin this exemplary depiction as a range on a single number line or axis,first range of values 512 may be defined on two or more dimensions,representing, for instance, a Cartesian product between a plurality ofranges, curves, axes, spaces, dimensions, or the like. First membershipfunction 508 may include any suitable function mapping first range 512to a probability interval, including without limitation a triangularfunction defined by two linear elements such as line segments or planesthat intersect at or below the top of the probability interval. As anon-limiting example, triangular membership function may be defined as:

${y\left( {x,a,b,c} \right)} = \left\{ \begin{matrix}{0,} & {{{for}x} > {c{and}x} < a} \\{\frac{x - a}{b - a},} & {{{for}a} \leq x < b} \\{\frac{c - x}{c - b},} & {{{if}b} < x \leq c}\end{matrix} \right.$

a trapezoidal membership function may be defined as:

${y\left( {x,a,b,c,d} \right)} = {\max\left( {{\min\ \left( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} \right)},0} \right)}$

a sigmoidal function may be defined as:

${y\left( {x,a,c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$

a Gaussian membership function may be defined as:

${y\left( {x,c,\sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

${y\left( {x,a,b,c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 5 , first fuzzy set 504 may represent any valueor combination of values as described above, including output from oneor more machine learning processes (e.g., user data classifier outputdata) . A second fuzzy set 516, which may represent any value which maybe represented by first fuzzy set 504, may be defined by a secondmembership function 520 on a second range 524; second range 524 may beidentical and/or overlap with first range 512 and/or may be combinedwith first range via Cartesian product or the like to generate a mappingpermitting evaluation overlap of first fuzzy set 504 and second fuzzyset 516. Where first fuzzy set 504 and second fuzzy set 516 have aregion 528 that overlaps, first membership function 508 and secondmembership function 520 may intersect at a point 532 representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set 504 and second fuzzy set 516. Alternatively oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 536 on first range 512 and/or second range 524, wherea probability of membership may be taken by evaluation of firstmembership function 508 and/or second membership function 520 at thatrange point. A probability at 528 and/or 532 may be compared to athreshold 540 to determine whether a positive match is indicated.Threshold 540 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 504 and second fuzzy set 516, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and a predetermined class, such aswithout limitation sub-quantifiers of a posting category, forcombination to occur as described above. Alternatively or additionally,each threshold may be tuned by a machine-learning and/or statisticalprocess.

Still referring to FIG. 5 , in an embodiment, outputs from an user dataclassifier may be compared to multiple class fuzzy sets representingelements of and/or pertaining to a compatibility criterion. Forinstance, one categorized bin containing an user data match between aparticular second identifier and first identifier may be represented byan individual fuzzy set that is compared to each of the multiple classfuzzy sets; and a degree of overlap exceeding a threshold between theindividual fuzzy set and any of the multiple class fuzzy sets may causeprocessor 104 and/or computing device 112 to rank the output datum ofthe user data classifier. For instance, in one embodiment there may betwo class fuzzy sets, representing a first output of user dataclassifier and a second output of user data classifier. First output ofuser data classifier may have a first fuzzy set; second output of userdata classifier may have a second fuzzy set; and inputs from acompatibility criterion may have an individual fuzzy set. Processor 104and/or computing device 112, for example, may compare an individualfuzzy set with each of first fuzzy set and second fuzzy set, asdescribed above, and rank user data classifier output datum based on thefirst and second fuzzy sets. Machine-learning methods as describedthroughout may, in a non-limiting example, generate coefficients used infuzzy set equations as described above, such as without limitation x, c,and σ of a Gaussian set as described above, as outputs ofmachine-learning methods. Likewise, subject-specific data may be usedindirectly to determine a fuzzy set, as the fuzzy set may be derivedfrom outputs of one or more machine-learning models that take thesubject-specific data directly or indirectly as inputs. Although anexemplary application for fuzzy set matching is described above, fuzzyset matching may be used for any classifications or associationsdescribed within this disclosure.

Referring now to FIG. 6 , a flow diagram of exemplary method 600 forusing machine learning to classify temporal sections. At step 605,method include using a computing device to identify, as described andefined in FIG. 1 , a plurality of temporal ranges, associated with aplurality of first identifiers and a plurality of sets of descriptivedata, wherein each temporal range of the plurality of temporal ranges isassociated a first identifier of the plurality of first identifiers, andeach first identifier of the plurality of first identifiers isassociated with a set of descriptive data of the plurality of sets ofdescriptive data. The computing device may be any computing devicedescribed through this disclosure, for example and with reference toFIG. 1 . In some embodiments, operators of a virtual event may set apredetermined time for identifying temporal ranges. For example, firstidentifiers may be required to submit their availability for interviewappointments a week before the career fair or during a specific timewhile attending the event. First identifiers may submit theiravailability using a network that the computing device may be connectedto in order to create temporal sections based on temporal ranges. Insome embodiments, a temporal range may include descriptive data, asdefined in FIG. 1 , as the technical background of a first identifier.For example, the company history, work focus, hiring criterion and thelike. At step 610, method including using the computing device togenerate a plurality of temporal sections, wherein generating furtherincludes dividing each temporal range into at least a temporal sectionof the plurality of temporal sections, as described in FIG. 1 . In someembodiments, creating the plurality of temporal sections is numericallimited. For example, the number of temporal sections per hour may belimited to a predetermined number to space out the availability ofrecruiters, allowing more second identifiers the chance to beinterviewed. The computing device may create temporal sections using amachine learning algorithm as disclosed in FIGS. 1 and 2 . For example,temporal ranges of the first identifiers may be an algorithm input and aplural temporal section associated the first identifiers may be theoutput. The algorithm may use a set of time limitation rules as part ofthe training data.

Still referring to FIG. 6 , method may include using a computing deviceto output, as a function of the created temporal sections, firstidentifier datum as defined in FIG. 1 . The computing device may beconfigured to send first identifier datum to second identifiers througha remote device. In some embodiments, a second identifier, as defined inFIG. 1 , may select the output of first identifier datum 124. Secondidentifier selection may include a first identifier classifier based ondescriptive data, as defined, and disclosed in FIG. 1 . In someembodiments, descriptive data may be the hiring requirements, workdescription, company history/description, and the like of a firstidentifier. In some embodiments, a plurality of first identifiertemporal sections may be a classifier input, a plurality of firstidentifier descriptive data as a part of the training data, and aplurality of categorized first identifiers as the output. For example, asecond identifier may select to only see the temporal sections of theplurality of first identifiers based on first identifier background,such as electrical engineering firms. A classification algorithm mayoutput the classified first identifiers matching that backgroundcategory for the computing device to display to the second identifier tosee the applicable temporal sections.

Still referring to FIG. 6 , at step 615, method includes using acomputing device to receive at least a second identifier, wherein thesecond identifier is associated with at least a temporal constraint anda set of second identifier data, for example and with reference to FIG.1 . In some embodiments, the computing device may acquire a temporalconstraint in the form of a bid by a second identifier. Bids, as definedin FIG. 1 , may include a second identifier uploading user data, asdefined in FIG. 1 , to a network which the computing device may accessto process a second identifier temporal constraint. In some embodiments,bids may include a second identifier requesting to reserve a specifictemporal section associated with a first identifier of the plurality offirst identifiers. In some embodiments, bids may require differentelements of user data from a second identifier for a temporal sectionassociated with a particular first identifier. For example, for a secondidentifier to create a temporal constraint for a specific firstidentifier out of the plurality of first identifiers, the secondidentifier may be required to submit in their bid specific documents,such as a cover letter and writing sample. Bidding may occur during aset time period at the event based on the number of second identifiersor first identifiers that actually attend the event. In someembodiments, the computing device may limit the number of temporalconstraints received from a particular second identifier. For example, asecond identifier may be limited to a set number of bids that may besubmitted throughout the event. A second identifier may also be limitedto a period in which bids may be placed. For example, Bids may only beaccepted before a deadline set by an event operator. In someembodiments, the user data from the plurality of second identifiers maybe classified against the descriptive data of the plurality of firstidentifiers, using any classification algorithm as described in FIGS. 1and 2 . For example, a bid with user data containing nursing experiencemay be categorized with first identifiers dealing in medical andhospitality work. A user data classifier may take a plurality of secondidentifier bids as an inputs, descriptive data of the plurality of firstidentifiers as training data, and output a plurality of matches betweensecond identifiers to first identifiers based on the user data. In someembodiments, user data classier may use a language processing module toparse elements of user data into a readable machine learning format, forexample and with reference to FIG. 1 . Classification of user data mayinclude ranking the user data classifier output datum in order ofcompatibility using a fuzzy inference system as describe in FIG. 5 .

Still referring to FIG. 6 , at step 620, method includes using acomputing device to classify, as a function of the inputs, a firstidentifier to a particular temporal section of the plurality of temporalsections as a function of the plurality of sets of descriptive data andat least a set of second identifier data. In some embodiments,classification may be based on event factors that include biddingpriority (e.g., first to bid first to reserve), bidding temporalconstraint quality (e.g., if a second identifier submitted the properaction data), bidding limitations (number of bids a second identifiercan place may correlate to the number of a appointments that may begenerated for the second identifier), temporal section amendments orabandonment by first identifiers, and the like. The computing device mayuse any machine learning process described throughout this disclosure,such as a classifier algorithm, to generate appointments between asecond identifier and a first identifier. An appointment classifier maytake the bids of the plurality of second identifiers as inputs andoutput a plurality of appointments between a second identifier and afirst identifier. The training data may include the plurality of eventfactors, output datum of user data classifier, and first identifierdatum to match each second identifier of the plurality of secondidentifiers to at least one first identifier for appointment purposes.

Still referring to FIG. 5 , at step 625, method includes using acomputing device to output particular temporal sections between aplurality of first identifiers and a plurality of second identifiers, asdefined and disclosed in FIG. 1 . In some embodiments, the particulartemporal sections may be outputted via electronic notification to secondidentifiers and first identifiers. The notification (or alert) mayinvolve an email, text message, automated voice call, and the like,among others sent to a remote device. As used in this disclosure, a“remote device” is any electronic and/or computing device that theplurality of second identifiers and first identifiers may receiveinformation on. For example, remote device 128 may be a laptop,cellphone, pager, smart watch, and the like. Temporal sections may beoutputted freely during the event or systematically. For example, ifthere is a set time for all second identifiers at the event to submittheir bids then the appointments may be generated after that set timeperiod has closed for all second identifiers at once. Alternatively, ifthere is no set time for second identifiers to bid then appointments maybe created between the second identifier and recruiter as they areselected throughout the event.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andapparatuses according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for using machine learning to classify to temporalsections, the apparatus comprising: at least a processor; and a networkinterface device communicatively connecting the apparatus to a network;a memory communicatively connected to the processor, the memorycontaining instructions configuring the at least a processor to:identify a plurality of temporal ranges, associated with a plurality offirst identifiers and a plurality of sets of descriptive data, wherein:each temporal range of the plurality of temporal ranges is associatedwith a first identifier of the plurality of first identifiers; and eachfirst identifier of the plurality of first identifiers is associatedwith a set of descriptive data of the plurality of sets of descriptivedata; generate a plurality of temporal sections, wherein each temporalrange is divided into at least a temporal section of the plurality oftemporal sections, wherein generating the plurality of temporal sectionscomprises: training, using first training data and a first machinelearning algorithm, a first machine learning model, wherein the firsttraining data includes a set of time limitation rules; and generating,using the trained first machine-learning model as a function of anexpected algorithmic loss relating temporal range inputs to temporalsection outputs, the plurality of temporal sections, wherein theplurality of temporal ranges is provided to the trained first machinelearning model as an input to output the plurality of temporal sectionsassociated with the plurality of first identifiers; receive a secondidentifier, wherein each second identifier of the plurality of secondidentifiers is associated with at least a temporal constraint receivedin the form of a bid, and a set of user data comprising at least aresume; classify a particular second identifier of the plurality ofsecond identifiers to at least a particular temporal section of theplurality of temporal sections generated by the first machine learningmodel, wherein classifying the second identifier comprises: training,using second training data and a second machine learning algorithm, asecond machine-learning model, wherein the second training datacomprises a plurality of event factors data and a plurality of firstidentifier data correlated with temporal constraint data; andgenerating, using the trained second machine learning model, the atleast a particular temporal section, wherein the particular secondidentifier is provided to the trained second machine learning model asan input to output the at least a particular temporal section; andoutput the at least a particular temporal section associated with theparticular second identifier upon establishing a communicativeconnection between the apparatus and a remote device using the networkinterface, wherein the at least a particular temporal section provides aschedule showing one or more generated appointments between theparticular second identifier and one or more first identifiers at anevent.
 2. The apparatus of claim 1, wherein generating a plurality oftemporal sections for a plurality of first identifiers is numericallimited.
 3. The apparatus of claim 1, wherein the generated plurality oftemporal sections of the first identifier is displayed based on a secondidentifier selection.
 4. The apparatus of claim 3, wherein the secondidentifier selection comprises a first identifier classifier based ondescriptive data.
 5. The apparatus of claim 1, wherein acquiring aplurality of temporal constraints from a plurality of second identifiercomprises user data from the plurality of second identifiers.
 6. Theapparatus of claim 5, wherein the user data from the plurality of secondidentifiers is classified against the descriptive data of the pluralityof first identifiers.
 7. The apparatus of claim 5, wherein memorycontains instructions further configuring processor to limit the numberof temporal constraints received from a particular second identifier. 8.The apparatus of claim 1, wherein acquiring a plurality of timeconstraints from a plurality of second identifiers is limited to apredetermined time.
 9. (canceled)
 10. The apparatus of claim 1, whereinoutputting temporal sections between a plurality of first identifiersand a plurality of second identifiers comprises a notification.
 11. Amethod for using machine learning to classify temporal sections, themethod comprising: identifying, using a computing device having anetwork interface, a plurality of temporal ranges, associated with aplurality of first identifiers and a plurality of sets of descriptivedata, wherein: each temporal range of the plurality of temporal rangesis associated with a first identifier of the plurality of firstidentifiers; and each first identifier of the plurality of firstidentifiers is associated with a set of descriptive data of theplurality of sets of descriptive data; generating, using the computingdevice, a plurality of temporal sections, wherein each temporal range isdivided into at least a temporal section of the plurality of temporalsections, wherein generating the plurality of temporal sectionscomprises: training, using first training data and a first machinelearning algorithm, a first machine learning model, wherein the firsttraining data includes a set of time limitation rules; and generating,using the trained first machine-learning model as a function of anexpected algorithmic loss relating temporal range inputs to temporalsection outputs, the plurality of temporal sections, wherein theplurality of temporal ranges is provided to the trained first machinelearning model as an input to output the plurality of temporal sectionsassociated with the plurality of first identifiers; receiving, using thecomputing device, a second identifier, wherein each second identifier ofthe plurality of second identifiers is associated with at least atemporal constraint received in the form of a bid, and a set of userdata comprising at least a resume; classifying, using the computingdevice, a particular second identifier of the plurality of secondidentifiers to at least a particular temporal section of the pluralityof temporal sections generated by the first machine learning model,wherein classifying the second identifier comprises: training, usingsecond training data and a second machine learning algorithm, a secondmachine-learning model, wherein the second training data comprises aplurality of event factors data and a plurality of first identifier datacorrelated with temporal constraint data; and generating, using thetrained second machine learning model, the at least a particulartemporal section, wherein the particular second identifier is providedto the trained second machine learning model as an input to output theat least a particular temporal section; and outputting, using thecomputing device, the at least a particular temporal section associatedwith the particular second identifier upon establishing a communicativeconnection between the computing device and a remote device associatedwith the particular second identifier using the network interface,wherein the at least a particular temporal section provides a scheduleshowing one or more generated appointments between the particular secondidentifier and one or more first identifiers at an event.
 12. The methodof claim 11, wherein generating a plurality of temporal sections for aplurality of first identifiers is numerically limited.
 13. The method ofclaim 11, wherein the generated plurality of temporal sections of thefirst identifier is displayed based on a second identifier selection.14. The method of claim 13, wherein the second identifier selectioncomprises a first identifier classifier based on descriptive data. 15.The method of claim 11, wherein acquiring a plurality of temporalconstraints from a plurality of second identifier comprises user datafrom the plurality of second identifiers.
 16. The method of claim 15,wherein the user data from the plurality of second identifiers isclassified against the descriptive data of the plurality of firstidentifiers.
 17. The method of claim 15, further comprising limiting, bythe computing device, the number of temporal constraints received from aparticular second identifier.
 18. The method of claim 11, whereinacquiring a plurality of time constraints from a plurality of secondidentifiers is limited to a predetermined time.
 19. (canceled)
 20. Themethod of claim 11, wherein outputting temporal sections between aplurality of first identifiers and a plurality of second identifierscomprises a notification.